Analysis of pharmacovigilance data for drug interactions or other causes of adverse events has been an important endeavor in order to maximize the safety of patients, and discovery of such interactions and other causes of adverse events as quickly and efficiently as possible is of utmost importance. While statistical methods to analyze pharmacovigilance data have been used extensively, many suffer from several deficiencies. For example, many of the statistical techniques used do not adequately account for the “bystander effect” in which interactions that appear to be caused by the presence of one drug or cause is really the result of the simultaneous presence of another drug or cause.
Furthermore, the known techniques of analysis of pharmacovigilance data also often suffer from the problem that the drug or other interactions (or information) that are present in the data (stored, for example, in a table in a database) are not intuitively or conveniently visualized in a display.